Quick Answer
The answer is recall. Recall is the correct metric because it measures the proportion of actual positive cases—in this scenario, equipment failures—that the model correctly identifies. In a highly imbalanced dataset with 99% negative examples, a model can achieve 99% accuracy by simply predicting the majority class for every instance, but this yields 0% recall for the rare failure class, making accuracy a dangerously misleading metric. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of evaluation metrics for imbalanced classification, a common trap where candidates default to accuracy without considering class distribution. The key insight is that recall focuses on capturing all positive instances, which is critical for rare event detection evaluation where missing a failure is far more costly than a false alarm. Memory tip: Recall = “Really find the rare ones”—think of it as the model’s ability to “recall” all the positive cases from the dataset.
This MLS-C01 practice question tests your understanding of modeling. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A company is building a binary classifier to predict equipment failure. The dataset has 99% negative (no failure) and 1% positive (failure) examples. The data scientist uses a random forest model with default settings. The model achieves 99% accuracy on the test set but fails to identify any actual failures. Which metric should the data scientist use to evaluate the model?
RMSE
Why wrong: RMSE is for regression, not classification.
R-squared
Why wrong: R-squared is for regression, not classification.
Recall
Recall measures the proportion of actual positives correctly identified, which is critical for imbalanced data.
Precision
Why wrong: Precision is useful but does not capture the failure to identify positives; recall is more relevant here.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Recall
Recall (sensitivity) measures the proportion of actual positive cases correctly identified. With 99% negative examples, a model can achieve 99% accuracy by simply predicting 'no failure' for all instances, but this yields 0% recall for the failure class. Since the goal is to detect rare failures, recall is the appropriate metric to evaluate the model's ability to find positive cases.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
RMSE
Why it's wrong here
RMSE is for regression, not classification.
R-squared
Why it's wrong here
R-squared is for regression, not classification.
Recall
Why this is correct
Recall measures the proportion of actual positives correctly identified, which is critical for imbalanced data.
Related concept
Read the scenario before looking for a memorised answer.
Precision
Why it's wrong here
Precision is useful but does not capture the failure to identify positives; recall is more relevant here.
Common exam traps
The trap here is that candidates see 99% accuracy and assume the model is performing well, failing to recognize that accuracy is a poor metric for imbalanced datasets, and they overlook recall as the metric that reveals the model's inability to detect the minority class.
Detailed technical explanation
In imbalanced classification, accuracy is misleading because a naive majority-class classifier can achieve high accuracy. Recall is calculated as TP/(TP+FN); with zero true positives, recall is 0%, directly exposing the model's failure to detect any failures. Random forest with default settings often uses a 0.5 probability threshold, which can be suboptimal for rare classes; adjusting the threshold or using class weights can improve recall.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
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FAQ
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
The correct answer is: Recall — Recall (sensitivity) measures the proportion of actual positive cases correctly identified. With 99% negative examples, a model can achieve 99% accuracy by simply predicting 'no failure' for all instances, but this yields 0% recall for the failure class. Since the goal is to detect rare failures, recall is the appropriate metric to evaluate the model's ability to find positive cases.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 2026
This MLS-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLS-C01 exam.
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